Virtual screening of chemical libraries is a common starting point for discovering ligands, such as drug leads. The primary tool for virtual screening is molecular docking, which is fast but makes a number of serious approximations about how small molecules interact with proteins. On the other hand, methods based on rigorous statistical physics are much slower and more accurate. We have recently derived a new statistical physics theory for binding that is based on multiple rigid structures of a receptor: implicit ligand theory Computational methods based on this theory have the potential to carefully compromise between the speed of docking and the accuracy of other rigorous methods. Here we develop and assess computational tools to predict (i) whether a molecule will bind to a protein or not, (ii how tightly it binds, and (iii) where it binds. Speci?cally, we will ?rst test whether binding o a single rigid structure is suf?cient to distinguish active from inactive molecules. Second, we wil assess the ability of different molecular simulation methods to generate receptor structures relevant to ligand binding and lead to accurate binding af?nity predictions. Third, we will develp algorithms to rank binding poses based on our theory. We anticipate that our methods will be used in the second stage of a virtual screen; after docking millions of compounds, one can use our methods with the top several thousand.